December 16, 2025 Dataset Open Access

Kieslich, Aaron Markus; Singh, Yerik; Palkowitsch, Martina; Starke, Sebastian; Hennings, Fabian; Troost, Esther Gera Cornelia; Krause, Mechthild; Bensberg, Jona; Lühr, Armin; Heinzelmann, Feline; Bäumer, Christian; Timmermann, Beate; Depauw, Nicolas; Shih, Helen A.; Löck, Steffen

Dublin Core Export

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  <dc:creator>Kieslich, Aaron Markus</dc:creator>
  <dc:creator>Singh, Yerik</dc:creator>
  <dc:creator>Palkowitsch, Martina</dc:creator>
  <dc:creator>Starke, Sebastian</dc:creator>
  <dc:creator>Hennings, Fabian</dc:creator>
  <dc:creator>Troost, Esther Gera Cornelia</dc:creator>
  <dc:creator>Krause, Mechthild</dc:creator>
  <dc:creator>Bensberg, Jona</dc:creator>
  <dc:creator>Lühr, Armin</dc:creator>
  <dc:creator>Heinzelmann, Feline</dc:creator>
  <dc:creator>Bäumer, Christian</dc:creator>
  <dc:creator>Timmermann, Beate</dc:creator>
  <dc:creator>Depauw, Nicolas</dc:creator>
  <dc:creator>Shih, Helen A.</dc:creator>
  <dc:creator>Löck, Steffen</dc:creator>
  <dc:date>2025-12-16</dc:date>
  <dc:description>This repository contains the outputs, model checkpoints and result data of our deep-learning-based experiments for the approximation of Monte-Carlo-simulated linear energy transfer distributions and uncertainty estimation, which build the foundation for the corresponding article.</dc:description>
  <dc:identifier>https://rodare.hzdr.de/record/4185</dc:identifier>
  <dc:identifier>10.14278/rodare.4185</dc:identifier>
  <dc:identifier>oai:rodare.hzdr.de:4185</dc:identifier>
  <dc:relation>url:https://www.hzdr.de/publications/Publ-42451</dc:relation>
  <dc:relation>url:https://www.hzdr.de/publications/Publ-42419</dc:relation>
  <dc:relation>doi:10.14278/rodare.4184</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/health</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/oncoray</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/rodare</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>proton radiotherapy</dc:subject>
  <dc:subject>linear energy transfer</dc:subject>
  <dc:subject>deep learning</dc:subject>
  <dc:subject>pencil beam scanning</dc:subject>
  <dc:subject>double scattering</dc:subject>
  <dc:subject>uncertainty quantification</dc:subject>
  <dc:title>Data publication: Deep learning for dose-averaged linear energy transfer estimation in pencil-beam scanning and double scattering proton plans with uncertainty-aware external validation</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>